Control every interaction, model, and agent from a single layer.
KRNL proposes a layer of policies, validations, traceability, and human oversight over every enterprise AI interaction.
Centralized policies
ActiveConfigurable guardrails
ActiveGoverned agents
ActivePath of every interaction
Usage per model
1 interaction under active human review
Conceptual operating example · does not reflect real metrics
Governed flow
The governed flow
Every request can follow a governance circuit before reaching a result: it can be validated, executed, and logged according to policies.
User / System
Any person, agent, or system can initiate a request.
KRNL Policy Engine
It reviews permissions and risk level before continuing.
Authorized model
The request can be routed to the authorized model according to the applicable policy.
Policy-based validation
The response can go through a verification before leaving.
Auditable record
Every interaction can be logged, with evidence available afterwards.
User / System
Any person, agent, or system can initiate a request.
KRNL Policy Engine
It reviews permissions and risk level before continuing.
Authorized model
The request can be routed to the authorized model according to the applicable policy.
Policy-based validation
The response can go through a verification before leaving.
Auditable record
Every interaction can be logged, with evidence available afterwards.
This is how the result of each circuit is recorded.
Example of a governed decision
Conceptual operating example · does not reflect real metrics
View evidenceRegulatory readiness
AI governance ready for auditing, data, and enterprise control.
KRNL makes it possible to define policies, log interactions, control access, and generate evidence about the use of agents, models, and data. This helps organizations operate AI with greater traceability and prepare for stricter regulatory requirements on the processing of personal data.
See how KRNL worksPolicies
It can define what each agent, model, or workflow does.
Access
It lets you control who uses which data, models, agents, or tools.
Traceability
Every interaction can be logged, with its associated model and rule.
Evidence
It can leave records available for internal review, IT, legal, compliance, or auditing.
What evidence KRNL can leave
Interaction log
Who used which agent, when, and what for.
Policy applied
Which rule allowed, blocked, or escalated an action.
Model used
Which model was involved in each execution.
Review available
Evidence available for consultation by IT, legal, compliance, or internal audit.
Capabilities
What KRNL controls
Five capabilities that work together, not separately.
Policies
Lets you define which agents, models, and data each area can use.
Guardrails
Helps stop out-of-policy actions before they reach the user or system.
Auditing
Inputs, outputs, models used, and decisions can be logged.
Costs
Lets you see consumption per model, agent, area, or use case.
Human control
It can escalate critical actions for review before executing them.
Operational evidence
Traceability designed for every decision
KRNL proposes a traceability model of who executed what, with which model, under which policy, and with what result.
Swipe to see the full table →
Time
Area / Agent
Model
Policy
Status
Evidence
Legal
Contracts Agent
Legal Confidentiality
Human reviewFinance
Expense Analysis
Cost policy
AllowedHR
Onboarding
HR policy
AllowedSupport
Tickets Agent
Data access policy
BlockedConceptual operating example · does not reflect real metrics
Operate AI with rules, evidence, and control.
KRNL lets you move from scattered tools to a governed, traceable, and secure AI operation.